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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading AI engineering initiatives, including the design and deployment of enterprise-grade AI solutions, while ensuring compliance, security, and operational excellence. Proven ability to mentor and develop technical teams, manage complex projects, and communicate effectively with stakeholders.
Highest-signal resume keywords
AI Engineering LeadershipEnterprise-Grade AI/ML Systems DeliveryPython Software DevelopmentCloud-Native Deployment TechnologiesMLOps and LLMOps Expertise
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine Learning EngineeringData EngineeringAPIsAutomated TestingCI/CDPyTorchTensorFlowDockerKubernetesGenerative AI Solutions
Soft Skills
Technical LeadershipCommunicationMentoringCollaborationStakeholder Engagement
Tools & Technologies
AzureGCPAWSMLflowHugging FaceSpark
Certifications & Qualifications
Bachelor's Degree in Computer ScienceMaster's Degree in Computer Science
Industry Keywords
AI EngineeringEnterprise SoftwareSaaS ProductsIndustrial AIComputer Vision
Tech Stack
Tools & technologiesAWSAzureCloudDockerGoogle Cloud PlatformKubernetesMicroservicesPythonPyTorchScikit-LearnSparkTensorflow
About the role
Key responsibilities & impact- Lead a global AI engineering capability for Honeywell Forge and AI
- Design, build, integrate, and operate enterprise-grade AI capabilities for Honeywell products
- Own team execution, portfolio direction, talent strategy, customer-facing technical leadership, and organizational scaling mechanisms
- Shape the AI engineering roadmap and convert product and business priorities into executable plans
- Guide architecture and implementation while ensuring reliability, scalability, security, responsible AI, compliance, and cost
- Lead end-to-end AI engineering from experimentation and technical validation through production release, operation, and continuous improvement
- Participate in architecture reviews, design decisions, code and implementation reviews, prototyping, troubleshooting, and critical engineering risk resolution
- Drive full-stack AI solutions spanning data and feature pipelines, model and LLM integration, APIs, microservices, orchestration, user-facing workflows, and cloud, on-premises, or edge deployment
- Establish engineering standards for source control, code review, automated testing, documentation, CI/CD/CT, reproducibility, release management, and safe rollback
- Guide production patterns for model and LLM serving, evaluation, observability, performance and drift monitoring, cost tracking, retraining, and operational support
- Collaborate with Data Engineering, Platform Engineering, SRE, IT, Security, and Compliance
- Ensure controls for responsible AI, privacy, data governance, model lineage, security, compliance, and risk management
- Promote reusable platforms, reference architectures, templates, and inner-source assets
- Lead, coach, and develop AI engineers through goal setting, feedback, performance management, career development, succession planning, and recognition
- Plan team capacity and skills; recruit, onboard, and retain engineering talent
- Develop technical leaders through mentoring, design reviews, playbooks, demonstrations, training, and communities of practice
- Coordinate cross-team work, dependencies, delivery risks, status, options, and recommendations for senior stakeholders
- Evaluate emerging AI technologies including machine learning, time-series, computer vision, NLP, Generative AI, RAG, fine-tuning, and Agentic AI
- Communicate architecture, delivery status, trade-offs, risks, and business impact to engineers, product teams, executives, customers, and partners
- Represent Honeywell in customer briefings, technical forums, conferences, industry panels, and partner discussions
- Govern the AI engineering portfolio, operating planning, budgeting, workforce and capacity planning, hiring priorities, delivery governance, and investment trade-offs
- Lead customer discovery engagements, technical workshops, executive briefings, and demonstrations
- Build relationships with business, product, and customer stakeholders to accelerate adoption
- Build and scale geographically distributed AI engineering teams
- Develop talent pipelines through university partnerships, graduate and internship programs, research relationships, and targeted hiring
- Create MLOps and LLMOps labs, playbooks, architecture reviews, communities of practice, and mentoring programs
- Foster invention and intellectual-property creation and transfer research outcomes into product engineering backlogs
Requirements
What you’ll need- Bachelor's degree from an accredited institution in Computer Science, Software Engineering, Electrical Engineering, AI/ML, or a related technical discipline
- 10+ years of relevant experience across software engineering, machine learning engineering, AI engineering, data engineering, or related product-development roles
- 4+ years of experience leading and developing technical teams, including direct people-management responsibility
- 5+ years of hands-on software development experience with Python and production engineering practices, including APIs, automated testing, version control, CI/CD, and design reviews
- Proven delivery and operation of enterprise-grade AI/ML systems, with experience moving solutions from experimentation into reliable production use
- Hands-on experience with modern AI/ML frameworks and data technologies such as PyTorch, TensorFlow, scikit-learn, Spark, MLflow, Hugging Face, or equivalent
- Experience with cloud-native engineering and deployment technologies such as Azure, GCP, or AWS; Docker; Kubernetes; and managed data or AI platforms
- Experience delivering Generative AI solutions, including RAG, LLM evaluation, model or API integration, orchestration, and Agentic AI patterns
- Strong knowledge of MLOps and LLMOps, including model lifecycle management, evaluation, production monitoring, reproducibility, governance, and retraining strategies
- Demonstrated ability to communicate complex engineering topics and trade-offs to technical teams, product leaders, customers, and executive stakeholders
- Master's degree in Computer Science, Software Engineering, AI/ML, or a related field is valued
- Experience building AI capabilities for multi-tenant SaaS products or other mission-critical enterprise software is valued
- Experience deploying and optimizing AI workloads across cloud, on-premises, and edge environments is valued
- Depth in relevant domains such as industrial AI, autonomous systems, time-series forecasting and optimization, computer vision, or intelligent agents is valued
- Experience creating shared AI platforms, reusable services, internal developer tooling, reference architectures, or engineering standards adopted across teams is valued
- A record of developing diverse technical talent and building inclusive, high-performing engineering teams is valued
- External technical presence through customer engagement, conference participation, publications, patents, standards work, or open-source contribution is valued
Benefits
Comp & perks- Work for a well-known brand with a continued focus on innovation and growth
- Join a dynamic team where most leaders are promoted from within
- A culture that fosters inclusion, diversity, and innovation
- Equal opportunity employer
- Reasonable accommodation for individuals with disabilities
- No relocation package